Typehint for Sklearn. (#6799)

python-package/setup.cfg

@@ -3,4 +3,5 @@ description-file = README.rst
 
 [mypy]
 ignore_missing_imports = True
-disallow_untyped_defs = True
+disallow_untyped_defs = True
+follow_imports = silent

python-package/xgboost/callback.py

@@ -276,6 +276,9 @@ class TrainingCallback(ABC):
     .. versionadded:: 1.3.0
 
     '''
+
+    EvalsLog = Dict[str, Dict[str, Union[List[float], List[Tuple[float, float]]]]]
+
     def __init__(self):
         pass
 
@@ -287,13 +290,11 @@ class TrainingCallback(ABC):
         '''Run after training is finished.'''
         return model
 
-    def before_iteration(self, model, epoch: int,
-                         evals_log: 'CallbackContainer.EvalsLog') -> bool:
+    def before_iteration(self, model, epoch: int, evals_log: EvalsLog) -> bool:
         '''Run before each iteration.  Return True when training should stop.'''
         return False
 
-    def after_iteration(self, model, epoch: int,
-                        evals_log: 'CallbackContainer.EvalsLog') -> bool:
+    def after_iteration(self, model, epoch: int, evals_log: EvalsLog) -> bool:
         '''Run after each iteration.  Return True when training should stop.'''
         return False
 
@@ -351,7 +352,7 @@ class CallbackContainer:
 
     '''
 
-    EvalsLog = Dict[str, Dict[str, Union[List[float], List[Tuple[float, float]]]]]
+    EvalsLog = TrainingCallback.EvalsLog
 
     def __init__(self,
                  callbacks: List[TrainingCallback],

python-package/xgboost/compat.py

@@ -1,6 +1,7 @@
 # coding: utf-8
 # pylint: disable= invalid-name,  unused-import
 """For compatibility and optional dependencies."""
+from typing import Any
 import sys
 import types
 import importlib.util
@@ -36,7 +37,7 @@ except ImportError:
 
     MultiIndex = object
     Int64Index = object
-    DataFrame = object
+    DataFrame: Any = object
     Series = object
     pandas_concat = None
     PANDAS_INSTALLED = False
@@ -109,10 +110,12 @@ except pkg_resources.DistributionNotFound:
 try:
     import sparse
     import scipy.sparse as scipy_sparse
+    from scipy.sparse import csr_matrix as scipy_csr
     SCIPY_INSTALLED = True
 except ImportError:
     sparse = False
     scipy_sparse = False
+    scipy_csr: Any = object
     SCIPY_INSTALLED = False
 
 

python-package/xgboost/core.py

@@ -96,7 +96,11 @@ def from_cstr_to_pystr(data, length) -> List[str]:
     return res
 
 
-def _convert_ntree_limit(booster, ntree_limit, iteration_range):
+def _convert_ntree_limit(
+    booster: "Booster",
+    ntree_limit: Optional[int],
+    iteration_range: Optional[Tuple[int, int]]
+) -> Optional[Tuple[int, int]]:
     if ntree_limit is not None and ntree_limit != 0:
         warnings.warn(
             "ntree_limit is deprecated, use `iteration_range` or model "
@@ -1234,7 +1238,7 @@ class Booster(object):
                 params += [('eval_metric', eval_metric)]
         return params
 
-    def _transform_monotone_constrains(self, value: Union[dict, str]) -> str:
+    def _transform_monotone_constrains(self, value: Union[Dict[str, int], str]) -> str:
         if isinstance(value, str):
             return value
 
@@ -1246,7 +1250,9 @@ class Booster(object):
         return '(' + ','.join([str(value.get(feature_name, 0))
                                for feature_name in self.feature_names]) + ')'
 
-    def _transform_interaction_constraints(self, value: Union[list, str]) -> str:
+    def _transform_interaction_constraints(
+        self, value: Union[List[Tuple[str]], str]
+    ) -> str:
         if isinstance(value, str):
             return value
 
@@ -1447,7 +1453,7 @@ class Booster(object):
         attr_names = from_cstr_to_pystr(sarr, length)
         return {n: self.attr(n) for n in attr_names}
 
-    def set_attr(self, **kwargs):
+    def set_attr(self, **kwargs: Optional[str]) -> None:
         """Set the attribute of the Booster.
 
         Parameters
@@ -1971,7 +1977,7 @@ class Booster(object):
             "Data type:" + str(type(data)) + " not supported by inplace prediction."
         )
 
-    def save_model(self, fname):
+    def save_model(self, fname: Union[str, os.PathLike]):
         """Save the model to a file.
 
         The model is saved in an XGBoost internal format which is universal among the

python-package/xgboost/dask.py

@@ -1028,7 +1028,8 @@ async def _direct_predict_impl(  # pylint: disable=too-many-branches
             # Somehow dask fail to infer output shape change for 2-dim prediction, and
             #  `chunks = (None, output_shape[1])` doesn't work due to None is not
             #  supported in map_blocks.
-            chunks = list(data.chunks)
+            chunks: Optional[List[Tuple]] = list(data.chunks)
+            assert isinstance(chunks, list)
             chunks[1] = (output_shape[1], )
         else:
             chunks = None
@@ -1633,7 +1634,7 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
         )
 
         if callable(self.objective):
-            obj = _objective_decorator(self.objective)
+            obj: Optional[Callable] = _objective_decorator(self.objective)
         else:
             obj = None
         model, metric, params = self._configure_fit(
@@ -1734,7 +1735,7 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
             params["objective"] = "binary:logistic"
 
         if callable(self.objective):
-            obj = _objective_decorator(self.objective)
+            obj: Optional[Callable] = _objective_decorator(self.objective)
         else:
             obj = None
         model, metric, params = self._configure_fit(